Introduction

1 2 3 4 5 6

Key Questions

  • How does HIV persist and migrate across CNS and peripheral tissues despite systemic viral suppression?

Overview

This presentation summarizes:

  • Viral phylogenetics & BEAST modeling
  • CNS reservoir compartmentalization
  • Migration dynamics across tissues & BBB



Cohort Workflow

Last Gift Cohort

Body Donation

Autopsy

Tissue Collection

HIV Sequencing

Phylogenetics and Modeling

OBJECTIVES

Blood Brain Interface

Image attribution: BioRender.
(click anywhere to close)

Objectives

Aim 1

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Aim 2

  • Identify CNS-specific clusters (reservoirs)
  • Characterize:
    • Composition
    • Timing
    • Renewal from periphery
    • Reseeding capacity into peripheral reservoirs

APPROACH

Aim 1

Data Processing

  • SGA HIV env clones across tissues
  • CSF biomarkers & clinical metadata
  • BEAST diffusion + phylogenetic modeling
  • Negative Binomial & Poisson models

Workflow

Aim 2

Data Processing

  • HIV env clones across participants & tissues
  • Cleanup & alignment
  • Phylogenetic reconstruction
  • Identify CNS-specific supported clusters
  • Bayesian skyline / skygrid diffusion models
  • Timing (tMRCA) of clusters
  • Migration flows between CNS clusters & other tissues
  • Modeling factors associated with migration

Workflow

Sampling

Note

  • Our analyses included 20 participants with sequences from both the CNS and Periphery.
  • A total of 1875 SGA clones were included from 49 distincts tissues.
  • Luminex CSF data available for 34 markers.

AIM 1: Viral Dynamics

Maximum Clade Credibility Tree

Time-Scaled Maximum Clade Credibility Tree

HIV Migration Networks

HIV Flow across the BBB through time

AIM 1: Reservoirs Dynamic and CSF Markers

Summary

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Approach

  • We compared a patient-level aggregated negative-binomial model (AGG-NB) with a state-level hierarchical negative-binomial model (STATE-NB) that accounted for variation across individual BEAST states.

Data Preparation

  • Aggregated (pid-level counts):We first fit an aggregated-count model (mod_agg_nb), where counts were summed across BEAST states for each participant.”
  • Longitudinal (state-level / transition-level): We then fit a state-level model (mod_long_nb), leveraging the full longitudinal BEAST reconstruction.

Exposure (log)

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Data Input

Exposure

Conceptual motivation

In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Equation

\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]

Model Code

data %>%
  mutate(
    pairs_cns    = ntissues_cns * (ntissues_cns - 1) / 2,
    pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
    
    exposure = case_when(
      migration_type == "cross_BBB" & direction == "CNS to Periph" ~ 
        n_states * nseq_cns * nseq_periph,
      
      migration_type == "cross_BBB" & direction == "Periphery to CNS" ~ 
        n_states * nseq_periph * nseq_cns,
      
      migration_type == "within_CNS" ~ 
        n_states * pairs_cns * (nseq_cns^2),
      
      migration_type == "within_peripheral" ~ 
        n_states * pairs_periph * (nseq_periph^2)
    )
  )

CSF Markers

Data Preparation for Migration Models

Summaries

  • Count migration/transition events from and toward the CNS and **within* CNS
  • Compute number of discrete viral states per run
  • Count SGA clones sampled in CNS and in Periphery

Exposure Definition

  • Aggregated: # states × # CNS sequences × # Periphery sequences
  • long format: # sequences from × # sequences to

Filtering

  • Exclude viremic individuals
  • Exclude individuals without CSF data

Data Transformations

  • Log-transform exposure
  • Log1p-transform CSF marker values and reservoir measures

Workflow

Model Specification: Negative Binomial GLMM

Notes

  • Outcome: count of CNS-related migration/transition events
  • Offset: log(exposure)
  • Predictor: log1p-transformed CSF marker
  • Random effect for participants

Equation

\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]

fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}
fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
      dna_level_from + dna_level_to +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}

Model Results: Aggregated (pid-level counts)

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.035 0.7967–1.35 0.795
GM-CSF 8 Ok 0.884 0.7446–1.05 0.161
GRO-alpha (CXCL1) 13 Ok 0.988 0.9808–1.00 0.001 **
IL-1α 1 Sparse Data 0.160 0.0022–11.58 0.401
IL-1β 5 Ok 0.806 0.6734–0.96 0.018 *
IL-1RA 12 Ok 1.000 0.9995–1.00 0.000 ***
IL-2 2 Sparse Data 0.418 0.2951–0.59 0.000 ***
IL-5 5 Ok 0.985 0.8082–1.20 0.885
IL-6 12 Ok 0.998 0.9971–1.00 0.000 ***
IL-7 13 Ok 0.356 0.0618–2.05 0.248
IL-8 (CXCL8) 13 Ok 0.998 0.9952–1.00 0.043 *
IL-9 1 Sparse Data 1.104 0.5813–2.10 0.762
IL-10 8 Ok 0.481 0.2551–0.91 0.023 *
IL-15 5 Ok 0.442 0.1617–1.21 0.112
IL-17A/CTLA-8 1 Sparse Data 0.409 0.0534–3.13 0.389
IL-18 13 Ok 1.009 0.9871–1.03 0.435
IL-31 1 Sparse Data 0.932 0.6748–1.29 0.668
IP-10 (CXCL10) 13 Ok 1.000 0.9967–1.00 0.841
MCP-1 (CCL2) 13 Ok 1.000 0.9985–1.00 0.908
MIP-1α (CCL3) 13 Ok 0.868 0.7918–0.95 0.002 **
MIP-1β (CCL4) 13 Ok 0.964 0.9420–0.99 0.002 **
RANTES (CCL5) 13 Ok 1.007 0.9176–1.11 0.881
SDF-1α 13 Ok 1.000 0.9993–1.00 0.798
TNF-α 2 Sparse Data 0.328 0.2148–0.50 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok 1.002 0.875–1.15 0.972
GM-CSF 11 Ok 0.849 0.752–0.96 0.008 **
GRO-alpha (CXCL1) 16 Ok 0.987 0.980–0.99 0.000 ***
IL-1α 1 Sparse Data 0.258 0.011–6.02 0.399
IL-1β 7 Ok 0.843 0.721–0.99 0.034 *
IL-1RA 15 Ok 1.000 1.000–1.00 0.007 **
IL-2 2 Sparse Data 0.374 0.283–0.49 0.000 ***
IL-5 7 Ok 0.893 0.678–1.18 0.420
IL-6 15 Ok 0.998 0.997–1.00 0.000 ***
IL-7 16 Ok 1.460 0.378–5.64 0.583
IL-8 (CXCL8) 16 Ok 0.999 0.998–1.00 0.140
IL-9 1 Sparse Data 1.331 0.731–2.42 0.349
IL-10 10 Ok 0.823 0.685–0.99 0.038 *
IL-15 7 Ok 0.249 0.119–0.52 0.000 ***
IL-17A/CTLA-8 2 Sparse Data 0.745 0.250–2.22 0.596
IL-18 16 Ok 1.008 0.991–1.03 0.359
IL-31 1 Sparse Data 0.911 0.689–1.20 0.509
IP-10 (CXCL10) 16 Ok 1.000 0.999–1.00 0.746
MCP-1 (CCL2) 16 Ok 1.000 0.999–1.00 0.987
MIP-1α (CCL3) 16 Ok 0.903 0.827–0.99 0.024 *
MIP-1β (CCL4) 16 Ok 0.987 0.969–1.01 0.157
RANTES (CCL5) 16 Ok 1.001 0.935–1.07 0.987
SDF-1α 16 Ok 1.000 0.999–1.00 0.296
TNF-α 3 Sparse Data 0.380 0.220–0.66 0.001 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok 1.079 0.9085–1.28 0.387
GM-CSF 11 Ok 0.853 0.7522–0.97 0.013 *
GRO-alpha (CXCL1) 16 Ok 0.989 0.9803–1.00 0.007 **
IL-1α 1 Sparse Data 0.295 0.0061–14.32 0.538
IL-1β 7 Ok 0.824 0.6595–1.03 0.089 .
IL-1RA 15 Ok 1.000 0.9995–1.00 0.000 ***
IL-2 2 Sparse Data 0.385 0.3005–0.49 0.000 ***
IL-5 7 Ok 0.913 0.7685–1.08 0.300
IL-6 15 Ok 0.998 0.9973–1.00 0.000 ***
IL-7 16 Ok 1.273 0.2751–5.89 0.757
IL-8 (CXCL8) 16 Ok 0.999 0.9970–1.00 0.241
IL-9 1 Sparse Data 1.235 0.7163–2.13 0.448
IL-10 10 Ok 0.794 0.6189–1.02 0.069 .
IL-15 7 Ok 0.286 0.1314–0.62 0.002 **
IL-17A/CTLA-8 2 Sparse Data 1.011 0.2677–3.82 0.987
IL-18 16 Ok 1.010 0.9911–1.03 0.313
IL-31 1 Sparse Data 0.955 0.7104–1.28 0.762
IP-10 (CXCL10) 16 Ok 1.000 0.9988–1.00 0.576
MCP-1 (CCL2) 16 Ok 1.000 0.9987–1.00 0.763
MIP-1α (CCL3) 16 Ok 0.883 0.7876–0.99 0.032 *
MIP-1β (CCL4) 16 Ok 0.988 0.9626–1.01 0.343
RANTES (CCL5) 16 Ok 1.024 0.9410–1.11 0.581
SDF-1α 16 Ok 1.000 0.9976–1.00 0.996
TNF-α 3 Sparse Data 0.358 0.2383–0.54 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok 1.024 0.84274–1.25 0.809
GM-CSF 11 Ok 0.912 0.74469–1.12 0.373
GRO-alpha (CXCL1) 16 Ok 0.986 0.97556–1.00 0.008 **
IL-1α 1 Sparse Data 0.001 0.00002–0.05 0.001 ***
IL-1β 7 Ok 0.881 0.69896–1.11 0.282
IL-1RA 15 Ok 0.999 0.99921–1.00 0.000 ***
IL-2 2 Sparse Data 0.411 0.24355–0.69 0.001 ***
IL-5 7 Ok 1.205 0.82781–1.75 0.331
IL-6 15 Ok 0.998 0.99624–1.00 0.032 *
IL-7 16 Ok 0.369 0.08496–1.61 0.184
IL-8 (CXCL8) 16 Ok 0.999 0.99734–1.00 0.512
IL-9 1 Sparse Data 0.562 0.26925–1.17 0.125
IL-10 10 Ok 0.999 0.72760–1.37 0.996
IL-15 7 Ok 0.279 0.07528–1.03 0.056 .
IL-17A/CTLA-8 2 Sparse Data 1.601 0.24895–10.29 0.620
IL-18 16 Ok 1.013 0.98636–1.04 0.334
IL-31 1 Sparse Data 0.882 0.64825–1.20 0.425
IP-10 (CXCL10) 16 Ok 1 0.99786–1.00 0.840
MCP-1 (CCL2) 16 NA Estimates 0.999 NA–NA NaN
MIP-1α (CCL3) 16 Wide CI >1e6 <1e-6–+Inf 0.963
MIP-1β (CCL4) 16 Ok 0.971 0.93720–1.01 0.108
RANTES (CCL5) 16 NA Estimates 0.933 NA–NA NaN
SDF-1α 16 Ok 1 0.99906–1.00 0.605
TNF-α 3 Sparse Data 0.445 0.19842–1.00 0.049 *
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

Model Results: Longitudinal / state-level data

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.050 0.925–1.19 0.447
GM-CSF 8 Ok 0.943 0.870–1.02 0.154
GRO-alpha (CXCL1) 13 Ok 0.996 0.992–1.00 0.076 .
IL-1α 1 Sparse Data 0.074 0.013–0.41 0.003 **
IL-1β 5 Ok 0.974 0.848–1.12 0.711
IL-1RA 12 Ok 1.000 1.000–1.00 0.000 ***
IL-2 2 Sparse Data 0.689 0.530–0.89 0.005 **
IL-5 5 Ok 0.990 0.899–1.09 0.831
IL-6 12 Ok 0.999 0.999–1.00 0.032 *
IL-7 13 Ok 1.515 0.581–3.95 0.395
IL-8 (CXCL8) 13 Ok 0.998 0.997–1.00 0.000 ***
IL-9 1 Sparse Data 0.785 0.551–1.12 0.178
IL-10 8 Ok 0.657 0.489–0.88 0.005 **
IL-15 5 Ok 0.702 0.424–1.16 0.169
IL-17A/CTLA-8 1 Sparse Data 0.660 0.235–1.85 0.430
IL-18 13 Ok 1.011 1.001–1.02 0.034 *
IL-31 1 Sparse Data 1.042 0.882–1.23 0.626
IP-10 (CXCL10) 13 Ok 1.000 0.999–1.00 0.963
MCP-1 (CCL2) 13 Ok 0.999 0.999–1.00 0.000 ***
MIP-1α (CCL3) 13 Ok 0.957 0.887–1.03 0.255
MIP-1β (CCL4) 13 Ok 0.984 0.969–1.00 0.034 *
RANTES (CCL5) 13 Ok 1.006 0.962–1.05 0.794
SDF-1α 13 Ok 1.000 0.999–1.00 0.930
TNF-α 2 Sparse Data 0.610 0.457–0.81 0.001 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Negative Binomial Models (per transition)
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.030 0.931–1.14 0.565
GM-CSF 10 Ok 0.912 0.849–0.98 0.013 *
GRO-alpha (CXCL1) 15 Ok 0.995 0.991–1.00 0.020 *
IL-1α 1 Sparse Data 0.744 0.069–7.97 0.807
IL-1β 7 Ok 0.890 0.776–1.02 0.095 .
IL-1RA 14 Ok 1.000 1.000–1.00 0.197
IL-2 2 Sparse Data 0.607 0.490–0.75 0.000 ***
IL-5 6 Ok 0.953 0.864–1.05 0.342
IL-6 14 Ok 0.999 0.999–1.00 0.025 *
IL-7 15 Ok 1.297 0.464–3.62 0.620
IL-8 (CXCL8) 15 Ok 0.999 0.998–1.00 0.387
IL-9 1 Sparse Data 1.121 0.794–1.58 0.516
IL-10 10 Ok 0.880 0.738–1.05 0.159
IL-15 6 Ok 0.528 0.346–0.80 0.003 **
IL-17A/CTLA-8 2 Sparse Data 1.072 0.469–2.45 0.870
IL-18 15 Ok 1.007 0.996–1.02 0.223
IL-31 1 Sparse Data 0.945 0.794–1.13 0.527
IP-10 (CXCL10) 15 Ok 1.000 0.999–1.00 0.862
MCP-1 (CCL2) 15 Ok 1.000 0.999–1.00 0.958
MIP-1α (CCL3) 15 Ok 0.917 0.854–0.99 0.018 *
MIP-1β (CCL4) 15 Ok 0.991 0.976–1.01 0.245
RANTES (CCL5) 15 Ok 1.004 0.957–1.05 0.875
SDF-1α 15 Ok 1.000 0.999–1.00 0.551
TNF-α 3 Sparse Data 0.574 0.418–0.79 0.001 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.030 0.936–1.13 0.542
GM-CSF 10 Ok 0.925 0.863–0.99 0.029 *
GRO-alpha (CXCL1) 15 Ok 0.995 0.991–1.00 0.026 *
IL-1α 1 Sparse Data 0.465 0.054–3.98 0.485
IL-1β 7 Ok 0.903 0.795–1.03 0.115
IL-1RA 14 Ok 1.000 1.000–1.00 0.013 *
IL-2 2 Sparse Data 0.624 0.510–0.76 0.000 ***
IL-5 6 Ok 0.965 0.878–1.06 0.455
IL-6 14 Ok 0.999 0.999–1.00 0.023 *
IL-7 15 Ok 1.306 0.503–3.39 0.584
IL-8 (CXCL8) 15 Ok 0.999 0.998–1.00 0.206
IL-9 1 Sparse Data 1.061 0.773–1.46 0.712
IL-10 10 Ok 0.891 0.760–1.04 0.153
IL-15 6 Ok 0.570 0.374–0.87 0.009 **
IL-17A/CTLA-8 2 Sparse Data 0.967 0.450–2.08 0.932
IL-18 15 Ok 1.007 0.997–1.02 0.167
IL-31 1 Sparse Data 0.958 0.815–1.13 0.599
IP-10 (CXCL10) 15 Ok 1.000 0.999–1.00 0.866
MCP-1 (CCL2) 15 Ok 1.000 0.999–1.00 0.796
MIP-1α (CCL3) 15 Ok 0.923 0.865–0.99 0.017 *
MIP-1β (CCL4) 15 Ok 0.990 0.976–1.00 0.149
RANTES (CCL5) 15 Ok 1.006 0.962–1.05 0.779
SDF-1α 15 Ok 1.000 1.000–1.00 0.671
TNF-α 3 Sparse Data 0.590 0.446–0.78 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.291 0.88–1.88 0.185
GM-CSF 10 Ok 0.702 0.35–1.42 0.325
GRO-alpha (CXCL1) 15 Ok 0.538 0.28–1.03 0.060 .
IL-1α 1 Sparse Data 0.661 0.51–0.86 0.002 **
IL-1β 6 Ok 0.950 0.51–1.77 0.871
IL-1RA 14 Ok 0.469 0.33–0.67 0.000 ***
IL-2 2 Sparse Data 0.583 0.36–0.93 0.025 *
IL-5 7 Ok 0.837 0.40–1.77 0.641
IL-6 14 Ok 0.513 0.28–0.93 0.029 *
IL-7 15 Ok 1.186 0.73–1.92 0.491
IL-8 (CXCL8) 15 Ok 0.770 0.52–1.13 0.185
IL-9 1 Sparse Data 0.640 0.47–0.86 0.004 **
IL-10 9 Ok 1.029 0.65–1.62 0.901
IL-15 7 Ok 0.676 0.43–1.06 0.085 .
IL-17A/CTLA-8 2 Sparse Data 1.236 0.85–1.79 0.264
IL-18 15 Ok 1.636 0.92–2.90 0.092 .
IL-31 1 Sparse Data 1.155 0.63–2.11 0.639
IP-10 (CXCL10) 15 Ok 1.166 0.83–1.64 0.377
MCP-1 (CCL2) 15 Ok 0.494 0.31–0.80 0.004 **
MIP-1α (CCL3) 15 Ok 0.811 0.44–1.50 0.502
MIP-1β (CCL4) 15 Ok 0.849 0.57–1.25 0.412
RANTES (CCL5) 15 Ok 0.988 0.65–1.49 0.953
SDF-1α 15 Ok 0.979 0.60–1.60 0.933
TNF-α 3 Sparse Data 0.555 0.32–0.97 0.038 *
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

AIM 1: Reservoirs Dynamic and Reservoirs Characteristics

Model Results: Longitudinal / state-level data

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify reservoir characteristics associated with dynamics
Associations between Reservoirs Measures and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.875 7.0e-01–1.1e+00 0.224
HIV DNA (destination) Ok 1.007 8.5e-01–1.2e+00 0.932
Viral Diversity (source) Ok >1e6 1.7e+05–+Inf 0.004 **
Viral Diversity (destination) Ok >1e6 9.4e-03–+Inf 0.139
Viral Divergence Wide CI 5.247 <1e-6–+Inf 0.888
Associations between Reservoirs Measures and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.158 1.0e+00–1.3e+00 0.043 *
HIV DNA (destination) Ok 0.959 8.4e-01–1.1e+00 0.525
Viral Diversity (source) Ok >1e6 5.6e+00–+Inf 0.029 *
Viral Diversity (destination) Wide CI 0.586 <1e-6–+Inf 0.951
Viral Divergence Wide CI 0.005 <1e-6–+Inf 0.605
Associations between Reservoirs Measures and counts of migration events across the BBB
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.163 1.1e+00–1.3e+00 0.003 **
HIV DNA (destination) Ok 0.917 8.4e-01–1.0e+00 0.056 .
Viral Diversity (source) Ok >1e6 1.0e+05–+Inf 0.000 ***
Viral Diversity (destination) Ok 0.193 <1e-6–1.1e+05 0.808
Viral Divergence Ok 0.031 <1e-6–9.3e+05 0.693
Associations between Reservoirs Measures and counts of migration events within CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.730 6.4e-01–8.4e-01 0.000 ***
HIV DNA (destination) Ok 1.103 9.8e-01–1.2e+00 0.098 .
Viral Diversity (source) Ok 2972.111 3.5e-05–+Inf 0.391
Viral Diversity (destination) Ok 25988.049 5.1e-03–+Inf 0.197
Viral Divergence Wide CI 57.962 <1e-6–+Inf 0.670

Thank you